57 lines
2.5 KiB
Python
57 lines
2.5 KiB
Python
"""Quick D1 regression test: HEAD_DIM=64 only, must match Stage C.
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Kernel outputs un-normalized O + LSE (D5a path)."""
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import torch, math
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import cutlass.cute as cute
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import cutlass.torch as ct
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import cuda.bindings.driver as cuda
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from dsv4.kernels.attention.fmha import FmhaKernel
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def test():
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torch.manual_seed(42)
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hd, n = 64, 128
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m = 128
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q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
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k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device='cuda')
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v = torch.randn(n, hd, dtype=torch.bfloat16, device='cuda')
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v_kernel = v.unsqueeze(-1)
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c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device='cuda')
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# FP32 reference (un-normalized + normalized)
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qf = q[:, :, 0].float()
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kf = k[:, :, 0].float()
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scale = 1.0 / math.sqrt(hd)
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attn_max = (qf @ kf.T * scale).max(dim=-1, keepdim=True)[0]
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attn_exp = torch.exp(qf @ kf.T * scale - attn_max)
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attn_sum = attn_exp.sum(dim=-1, keepdim=True)
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ref_unnorm = attn_exp @ v.float()
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ref_norm = (attn_exp / attn_sum) @ v.float()
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lse_tensor = torch.zeros(m, 1, 1, dtype=torch.float32, device='cuda')
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mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
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mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
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mV = ct.from_dlpack(v_kernel).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel))
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mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
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mLSE = ct.from_dlpack(lse_tensor).mark_layout_dynamic(leading_dim=ct.get_leading_dim(lse_tensor))
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stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
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# normalize=False: kernel outputs un-normalized O + LSE
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kernel = FmhaKernel(head_dim=hd, s_k=n, normalize=False)
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print(f'hd={hd}, n={n}: Compiling...', flush=True)
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compiled = cute.compile(kernel, mQ, mK, mV, mC, stream, mLSE)
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compiled(mQ, mK, mV, mC, stream, mLSE)
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torch.cuda.synchronize()
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out_unnorm = c[:, :, 0].float()
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out_norm = out_unnorm / attn_sum # external normalization using row_sum
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cos_unnorm = torch.nn.functional.cosine_similarity(
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out_unnorm.flatten().unsqueeze(0), ref_unnorm.flatten().unsqueeze(0)
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).item()
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cos_norm = torch.nn.functional.cosine_similarity(
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out_norm.flatten().unsqueeze(0), ref_norm.flatten().unsqueeze(0)
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).item()
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print(f'hd={hd}, n={n}: cos_unnorm {cos_unnorm:.6f} cos_norm {cos_norm:.6f} {"PASS" if cos_norm >= 0.99 else "FAIL"}')
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if __name__ == '__main__':
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test()
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